1安裝python庫

使用pip安裝openai庫，注意gpt3.5-turbo模型需要python>=3.9的版本支持，本文演示的python版本是python==3.10.10

pip install openai

2創(chuàng)建api key

需要提前在openai官網(wǎng)上注冊好賬號，然后打開https://platform.openai.com/account/api-keys就可以創(chuàng)建接口keys

每個賬號注冊完成會有18美元在里面，每次調(diào)用api，就會花費(fèi)里面的余額，注意余額也有過期時間。

3費(fèi)用介紹

調(diào)用gpt3.5-turbo的費(fèi)用如下，可以看到每1000個token是0.002美元

關(guān)于token，官方也給出了說法

Multiple models, each with different capabilities and price points. Prices are per 1,000 tokens. You can think of tokens as pieces of words, where 1,000 tokens is about 750 words. This paragraph is 35 tokens.

原意就是1000個token大概是750個單詞，而上面這段話有35個token。所以，不用過于擔(dān)心這個費(fèi)用的問題。

4 API調(diào)用

官方給出了兩種調(diào)用Api的方法，分別是url請求和調(diào)用python包。

【1】使用curl工具請求

curl https://api.openai.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -d '{
     "model": "gpt-3.5-turbo",
     "messages": [{"role": "user", "content": "Say this is a test!"}],
     "temperature": 0.7
   }'

請求后返回的格式為：

{
   "id":"chatcmpl-abc123",
   "object":"chat.completion",
   "created":1677858242,
   "model":"gpt-3.5-turbo-0301",
   "usage":{
      "prompt_tokens":13,
      "completion_tokens":7,
      "total_tokens":20
   },
   "choices":[
      {
         "message":{
            "role":"assistant",
            "content":"\n\nThis is a test!"
         },
         "finish_reason":"stop",
         "index":0
      }
   ]
}

【2】調(diào)用python包

import openai

openai.api_key="OpenAI api key"
messages = []
system_message = input("What type of chatbot you want me to be?")
system_message_dict = {
    "role": "system",
    "content": system_message
}
messages.append(system_message_dict)
message = input("輸入想要詢問的信息: ")
user_message_dict = {
    "role": "user",
    "content": message
}
messages.append(user_message_dict)
response=openai.ChatCompletion.create(
  model="gpt-3.5-turbo",
  messages=messages
)
print(response)
reply = response["choices"][0]["message"]["content"]
print(reply)

請求包返回的結(jié)果：

{
  "choices": [
    {
      "finish_reason": "stop",
      "index": 0,
      "message": {
        "content": "\u4e0b\u9762\u662f\u4e00\u4e2aJava\u5b9e\u73b0\u7684\u96ea\u82b1\u7b97\u6cd5\u751f\u6210\u5206\u5e03\u5f0fID\u7684\u4ee3\u7801\uff1a\n\n```java\npublic class SnowflakeIdGenerator {\n    // \u5f00\u59cb\u65f6\u95f4\u6233\uff0c\u53ef\u4ee5\u6839\u636e\u9700\u8981\u81ea\u884c\u4fee\u6539\n    private final static long START_TIMESTAMP = 1480166465631L;\n  \n    // \u6bcf\u90e8\u5206\u5360\u7528\u7684\u4f4d\u6570\n    private final static long SEQUENCE_BIT = 12; // \u5e8f\u5217\u53f7\u5360\u7528\u7684\u4f4d\u6570\n    private final static long MACHINE_BIT = 5;   // \u673a\u5668\u6807\u8bc6\u5360\u7528\u7684\u4f4d\u6570\n    private final static long DATACENTER_BIT = 5;// \u6570\u636e\u4e2d\u5fc3\u5360\u7528\u7684\u4f4d\u6570\n  \n    // \u6bcf\u90e8\u5206\u7684\u6700\u5927\u503c\n    private final static long MAX_DATACENTER_NUM = ~(-1L << DATACENTER_BIT);\n    private final static long MAX_MACHINE_NUM = ~(-1L << MACHINE_BIT);\n    private final static long MAX_SEQUENCE = ~(-1L << SEQUENCE_BIT);\n  \n    // \u6bcf\u90e8\u5206\u5411\u5de6\u7684\u4f4d\u79fb\n    private final static long MACHINE_LEFT = SEQUENCE_BIT;\n    private final static long DATACENTER_LEFT = SEQUENCE_BIT + MACHINE_BIT;\n    private final static long TIMESTAMP_LEFT = DATACENTER_LEFT + DATACENTER_BIT;\n  \n    private long datacenterId;  // \u6570\u636e\u4e2d\u5fc3\u6807\u8bc6\n    private long machineId;     // \u673a\u5668\u6807\u8bc6\n    private long sequence = 0L; // \u5e8f\u5217\u53f7\n    private long lastTimestamp = -1L; // \u4e0a\u6b21\u751f\u6210ID\u7684\u65f6\u95f4\u6233\n  \n    public SnowflakeIdGenerator(long datacenterId, long machineId) {\n        if (datacenterId > MAX_DATACENTER_NUM || datacenterId < 0) {\n            throw new IllegalArgumentException(\"Datacenter ID can't be greater than \" + MAX_DATACENTER_NUM + \" or less than 0\");\n        }\n        if (machineId > MAX_MACHINE_NUM || machineId < 0) {\n            throw new IllegalArgumentException(\"Machine ID can't be greater than \" + MAX_MACHINE_NUM + \" or less than 0\");\n        }\n        this.datacenterId = datacenterId;\n        this.machineId = machineId;\n    }\n  \n    public synchronized long nextId() {\n        long timestamp = timeGen();\n  \n        // \u5982\u679c\u5f53\u524d\u65f6\u95f4\u5c0f\u4e8e\u4e0a\u6b21ID\u751f\u6210\u7684\u65f6\u95f4\u6233\uff0c\u8bf4\u660e\u7cfb\u7edf\u65f6\u949f\u56de\u9000\u8fc7\uff0c\u8fd9\u4e2a\u65f6\u5019\u5e94\u5f53\u629b\u51fa\u5f02\u5e38\n        if (timestamp < lastTimestamp) {\n            throw new RuntimeException(\"Clock moved backwards. Refusing to generate id for \" + (lastTimestamp - timestamp) + \" milliseconds\");\n        }\n  \n        // \u5982\u679c\u662f\u540c\u4e00\u65f6\u95f4\u751f\u6210\u7684\uff0c\u5219\u8fdb\u884c\u6beb\u79d2\u5185\u5e8f\u5217\u53f7\u9012\u589e\n        if (lastTimestamp == timestamp) {\n            sequence = (sequence + 1) & MAX_SEQUENCE;\n            // \u5982\u679c\u6beb\u79d2\u5185\u5e8f\u5217\u53f7\u5df2\u7ecf\u8fbe\u5230\u6700\u5927\uff0c\u5219\u6beb\u79d2\u5185\u5e8f\u5217\u53f7\u91cd\u7f6e\u4e3a0\uff0c\u91cd\u65b0\u751f\u6210\u4e0b\u4e00\u4e2aID\n            if (sequence == 0L) {\n                timestamp = tilNextMillis(lastTimestamp);\n            }\n        }\n        // \u65f6\u95f4\u6233\u6539\u53d8\uff0c\u6beb\u79d2\u5185\u5e8f\u5217\u53f7\u91cd\u7f6e\u4e3a0\n        else {\n            sequence = 0L;\n        }\n  \n        // \u4e0a\u6b21\u751f\u6210ID\u7684\u65f6\u95f4\u622a\n        lastTimestamp = timestamp;\n  \n        // \u79fb\u4f4d\u5e76\u901a\u8fc7\u6216\u8fd0\u7b97\u62fc\u5230\u4e00\u8d77\u7ec4\u621064\u4f4d\u7684ID\n        return ((timestamp - START_TIMESTAMP) << TIMESTAMP_LEFT)\n                | (datacenterId << DATACENTER_LEFT)\n                | (machineId << MACHINE_LEFT)\n                | sequence;\n    }\n  \n    /*\n     * \u5185\u90e8\u65b9\u6cd5\uff0c\u751f\u6210\u5f53\u524d\u65f6\u95f4\u6233\n     */\n    private long timeGen() {\n        return System.currentTimeMillis();\n    }\n  \n    /*\n     * \u5185\u90e8\u65b9\u6cd5\uff0c\u7b49\u5f85\u4e0b\u4e00\u4e2a\u6beb\u79d2\u7684\u5230\u6765\n     */\n    private long tilNextMillis(long lastTimestamp) {\n        long timestamp = timeGen();\n        while (timestamp <= lastTimestamp) {\n            timestamp = timeGen();\n        }\n        return timestamp;\n    }\n}\n```\n\n\u8fd9\u4e2a\u7c7b\u7684\u4f7f\u7528\u65b9\u6cd5\u5982\u4e0b\uff1a\n\n```java\nSnowflakeIdGenerator idGenerator = new SnowflakeIdGenerator(1, 1);\nlong id = idGenerator.nextId();\n```",
        "role": "assistant"
      }
    }
  ],
  "created": 1680359622,
  "id": "chatcmpl-70WaUhGWydy5fBxdlrs9fhqMj7kqN",
  "model": "gpt-3.5-turbo-0301",
  "object": "chat.completion",
  "usage": {
    "completion_tokens": 923,
    "prompt_tokens": 33,
    "total_tokens": 956
  }
}

5 補(bǔ)充

對于請求體messages中的role字段說明。
這里有三種角色，分別是system、user和assistant

• system：可以幫助設(shè)置assistant的行為，在gpt-3.5-turbo-0301模型中還沒有比較多地關(guān)注這個設(shè)置，在未來的模型中會比較關(guān)注
• user：這個是本次用戶輸入想要回答的一個問題。
• assistant：用于存儲之前用戶的歷史回答。

這三種角色相互依賴構(gòu)成的一組輸入信息，構(gòu)成了上下文的形式。

參考文章：
[1] https://openai.com/pricing
[2] https://platform.openai.com/account/api-keys
[3] https://platform.openai.com/docs/api-reference/introduction
[4] https://platform.openai.com/docs/introduction/overview文章來源地址http://www.zghlxwxcb.cn/news/detail-463896.html

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